In the beginning God created the heavens and the earth. Now the earth was formless and empty, darkness was over the surface of the

deep, and the Spirit of God was hovering over the waters. And God said, "Let there be light," and there was light. God saw that the light was good, and he separated the light from the darkness. God called the light "day," and the darkness he called "night." And there was

evening, and there was morning--the first day.

Genesis Genesis

IntroductionIntroduction

VisionVision : Source :Light, Sound, Heat,,

“ Wide range of wave can be Imaging Source”Human : EyeBat : High Frequency SoundSnake : HeatPredator : IR Image

Color : B&W, Full Color, Special BandwidthRange : Forward, 360o Coverage

Humans are Visual Creatures! Humans are Visual Creatures!

Can you explain Color Blue to the Blind? Can you make the Blind explain Color Blue?

Imagine ≈ “Looks Like”

Image ? ►► Reprocessed Information of Real or Imaginary Material

biased his/her Conceptual Background►► A kind of Language represented by Graphical Symbol

Artistic Image : Drawing, Photography, Technical Image : Scientific, Engineering, Machine,

“God created man in his own Image”

Model and Actual Satellite Data

Day 20 (11 September '94)Day 40 (1 October '94)

Day 56 (17 October '94)

Ozone Column Amount in AntarcticOzone Column Amount in Antarctic

Black Hole MergeBlack Hole Merge

After that.After that.

Great Success of Apollo 11 Mission to Moon?

Scientific Instrument makes Real World into Scientific Image

e.g) Thermometer : Temperature -> Number Abyss or Space Mission :

Success of Mission = Image QualityAtomic Image of Crystals

Multi Fourier Transformed Image by Electron Beam

In Digital Image , N by N Image with RGB Color : M bit DataCase of Information : N22M

128 by 128 Image with 8bit GrayMax Information = 4194304 ≈ 4 Million Case

Reliable Image Information?Reliable Image Information?

Scientific Image

Examples of ReliabilityExamples of Reliability

1. TEM Image2. Woman Portrait3. Curved Line4. Geometry Illusion5. Black White Spots6. Gray Scale

TEM Image

TEM Image Simulated CBED Pattern

Woman PortraitWoman Portrait

Curved LinesCurved Lines

Geometry IllusionGeometry Illusion

Black & White SpotsBlack & White Spots

Gray Scale Value ?Gray Scale Value ?

B is Brighter than A?B is Brighter than A?

Comparison of A & BComparison of A & B

““Information from Human Vision may be Wrong Information from Human Vision may be Wrong ““Image Processing is Required!!!Image Processing is Required!!!

ImageImage TransformationTransformation

Acquired Real Image Fourier Transformed Image

Image AcquisitionImage Acquisition

1. Radio Wave2. acoustic Wave3. Electron Beam Diffraction4. CBED & LACBED5. 3D Sonogram6. Camouflage

Radio Astronomy Images of NGC1265Radio Astronomy Images of NGC1265. .

Experiment Simulation

CBED(LACBED) in CBED(LACBED) in SiSi (111)(111)

Camera TypeCamera TypeCamera :

Transform Image into Electric Signal or Permanent Material

Analog : Film, Vidicon (CRT Type Input Device)- Easy & Cheap Device- Various Signal Standard : RS-xxx, NTSC, PAL ,,

<Problem>- Distortion Problem (Pin Cushion, Barrel) - Interlacing, Bloom, Vignetting

Digital : CCD ,,- Expensive , Current Reasonably Cheap- Color Space Based Standard (No Hardware Standard)- No Distortion Problem- Different RGB Gain

Human Eye ResponseHuman Eye Response

Response of photographic film. The "H&D" curve includes the linear response range in which the slope distinguishes high- and low-contrast films. High contrast means that a small range of exposure causes a large change in film density. Density is defined as the based en logarithm of the fraction of incident light that is transmitted.

Response of Photographic Film.Response of Photographic Film.

Response of a light sensor. Gamma values greater of less than 1.0 expand or compress the contrast range at the dark or light end of the range.

Response of Light Sensor (Gamma)Response of Light Sensor (Gamma)

►► Image Processing Image Processing : Drawing, Film Photograph, Voice, Human,, : Drawing, Film Photograph, Voice, Human,,

►► Digital Image ProcessingDigital Image Processing: Computer Vision, Digital Photograph, : Computer Vision, Digital Photograph,

►► Digital Image Processing inDigital Image Processing inMaterial Science & EngineeringMaterial Science & Engineering: MD, Optical, SEM, TEM, Auger, ESCA,XPS,,,,: MD, Optical, SEM, TEM, Auger, ESCA,XPS,,,,

Image Procession in Materials FieldImage Procession in Materials FieldIn 21C , Most of Image is Digital except Artistic Image

Digital Image Processing Steps1. Acquisition & Storage based on PixelPixel2. Reproduction in Real Space3. Enhancement & Correction4. Measurement5. Processing in Virtual Space (Transformation)6. Modeling

In Digital Image ,N by N Image with Gray M bit Data

Case of Information : N22M

N by N Image with RGB Color : M bit DataCase of Information : N223M

128 x 128 with 8bit Gray : 4194304 ≈ 4 Million128 x 128 with 3x8bit RGB Color : 2.74878 1011 ≈ 200 Billion

Pixel Resolution : - Number of Pixels- Number of Pixel Levels

Pixel : Basic Unit of Digital Image : PicPicture ElElement

PixelPixel

Gray Scale DigitizationGray Scale Digitization

A grey-scale image digitized from a metallographic microscope and its brightness histogram(while at the left, dark at the right). A bright reflection within he camera tube causes the automatic gain circuit to shift the histogram, even though the bright spot is not within the digitized area. This shifting would cause the same structure to have different grey values in successive images.

Digitization of an analog voltage signal such as one line in a video image(top) produces a series of numbers that represent a series of steps equal in time and rounded to integral multiples of the smallest height increment(bottom)

Digitization of Analog Voltage SignalDigitization of Analog Voltage Signal

Pixel Size

Four representations of the same image, with variation in the number of pixels used:a) 256×256; b) 128×128 c) 64×64; d) 32×32.In all cases, a full 256 grey values are retained. Each step in coarsening of the image is accomplished by averaging the brightness of the region

covered by the larger pixels.

Number of PixelsNumber of Pixels

Four representations of the same image, with variation in the number of grey levels used:a) 32; b) 16; c) 8; d) 4In all cases, a full 256×256 array of pixels is retained. Each step in the coarsening of the image is accomplished by rounding the brightness of

the original pixel value.

Number of Pixel LevelsNumber of Pixel Levels

Averaging of a noisy (low photon-intensity) image (light microscope image of bone marrow):a) one frame; b, c, d) addition of 4, 16 and 256 frames

Averaging Noisy ImageAveraging Noisy Image

Linear Expansion of HistogramLinear Expansion of Histogram

Unsharp masking:a) a telescope image of the Orion nebula originally recorded on film;b) the same image using unsharp masking. An out-of focus photographic print is made onto negative material, which is then placed on the

original to reduce the exposure in bright areas when the final print is made. This process reduces the overall contrast so that local variations show. Laplacian filtering performs the same function in digital image analysis.

UnsharpUnsharp MaskingMasking

scanning electron microscope (SEM) focuses a fine beam of electrons on the specimen, producing various signals that may be used for imaging as the beam is scanned in a raster pattern.

Electron MicroscopeElectron Microscope

SEMSEM

TEMTEM

Transmitted Beam

Secondary Electron

. The striped pattern reveals ferromagnetic domains, in which the electron spins of the atoms are aligned in one of two possible directions.

TEM image of a thin metal foil of cobaltTEM image of a thin metal foil of cobalt

TEM image of colloidal gold particle on an amorphous carbon substrate

TEM image of Colloidal Gold ParticleTEM image of Colloidal Gold Particle

Scanning electron microscope images of a mineral. The secondary and backscattered electron images delineate the various structures, and the silicon, iron, copper and silver X-ray images show which structures contain those elements

Scanning Electron Microscope ImagesScanning Electron Microscope Images

Color ScienceColor ScienceColor Space : RGB, CMYK, YIQ(YUV), ,, Various Space

Light Element : Red, Green, Blue

Y = 0.299R + 0.587G + 0.114BI = 0.596R – 0.274G – 0.322BQ = 0.211R – 0.523G + 0.312 B

R = 1.000 Y + 0.956 I + 0.621 QG = 1.000 Y – 0.272 I – 0.647 QB = 1.000 Y – 1.106 I + 1.703 Q

YIQ Code : CRT Representation Code (Television Set)Y : Luminance Used for B/W RepresentationI, Q(U, V) : Color Signal

CIE Chromaticity : First Encoding Scheme to Human EyesReference of Color Encoding Space (Commission International de L’Eclairage)

RGB color space, showing the additive progression from Black to White. Combining Red and Green produces Yellow; Green plus Blue produces Cyan, and Blue plus Red produces Yellow. Grey lie along the cube diagonal. Cyan, Yellow and Magenta are subtractive primaries used in printing, which if subtracted fromWhite leave Red, Blue and Green, respectively.

The CIE chromaticity diagram. The dark outline contains visible colors, which are fully saturated along the edge. Numbers give the wavelength of light in nanometers. The inscribed triangle shows the colors that typical color CRTs can produce by mixing of red, green and blue.

RGB Color SpaceRGB Color Space CIE DiagramCIE Diagram

YUV Color Representation in Video Transmission:

U : Green - Magenta V : blue minus yellowFamiliar Color Wheel

•

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BGR

baL

02/12/16/226/26/2

3/13/13/1

LL--aa--b Spaceb Space

Similar to H-S-I spaceCircular Space : Easy Mathematics

Bi-conic representation. Grey lie along the central axis. Distance from the axis gives the Saturation, while direction specifies the Hue.

HueHue--SaturationSaturation--Intensity SpaceIntensity Space

221 &tan baSabH +=

= −

)/()()(2/)2(

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RGRBGBRGBz

RGifRGif

zz

H

BGRBGRS

BGRI

−−+−

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π

Useful for Image Processing : Separation of Color informationsimilar with Human Visual Response

Color Separations from a Color Image

a) Original;b) red component;c) reen component;d) blue component;e) hue component;f) intensity component;g) saturation component

(1µm section of pancreas, polychromatic stain)

a) original;b) hue;c) intensity;d) saturation;e) luminance(Y);f) U image

(green=magenta);g) V image (blue-yellow).

Color Separations from a Light Microscope Image of stained biological tissue

3D Reconstruction3D Reconstruction

1. Voxel

2. Serial Section

3. Stereography

Voxels (volume elements) are ideally cubic for processing and measurement of 3D images.

Multiple Planes of Pixels fill 3D space.Multiple Planes of Pixels fill 3D space.

Interference Microscope.Interference Microscope. AFM.AFM.

AFM image of a Knoop hardness indentation

a) range;

b) rendered

c) isometric presentation

Serial section images formed by transmission Confocal scanning laser scanning laser microscopy(CSLM). These are selected views from a series

of sections through the leg joint of a head louse, with section thickness less than 0.5 μm.

Serial Section ImagesSerial Section Images

Light microscope image of section through a colored enamel coating applied to steel.

Example of Serial Projection ImageExample of Serial Projection Image

Transmission electron microscope image of latex spheres in a thick, transparent section

Possibility of 3-D Microstructure Reconstruction from 2-D Stacks

Possibility of 3Possibility of 3--D Microstructure D Microstructure Reconstruction from 2Reconstruction from 2--D StacksD Stacks

Serial milling by uniform thickness followed by 2-D microstructure imaging → 3-D microstructure reconstruction

A. Wilson, Presentations at OIM Academy

characteristic dimension of cm.The section planes can be positioned arbitrarily and moved to reveal the internal structure.

characteristic dimension of μm, the dark regions are voids. The poorer resolution in the vertical direction is due to the spacing of the image planes, which is greater than the lateral pixel resolution within each plane.

A human head imagedby magnetic resonance (MRI)

A sintered ceramic imagedby X-ray tomography

Stereoscopic Depth PerceptionStereoscopic Depth Perception

a) The relative distance to each feature identified in both the left and right eye views is given by differences in the vergence angles by which the eyes must rotate inwards to bring each feature to the central fovea in each eye. This is accomplished one feature at a time. Viewing stereo pair images provide the same visual cues to the eyes and produces the same interpretation.b) Measurement of images to obtain actual distances uses the different parallax displacements of the features in two images. The distance between the two view points must be known. Identifying the same feature in both images is the greatest difficulty for automated analysis.

The specimen is the surface of a leaf; the two images were obtained by tilting the beam incident on the specimen by 8° to produce two points of view.

Stereo Pair Images from SEMStereo Pair Images from SEM

To view the image, use glasses with the red filter in front of the left eye, and either a green or blue filter in front of the right eye.

Color (Red/Cyan) Stereo ImageColor (Red/Cyan) Stereo Image